2025-05-06 オークリッジ国立研究所（ORNL）
ORNL’s “black box” grid modeling method protects proprietary information about the inner workings of equipment while providing an accurate simulation of grid behavior at least 10 times faster than conventional methods. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy
＜関連情報＞
- https://www.ornl.gov/news/research-reveals-hidden-gifts-black-box-modeling-grid-behavior
- https://ieeexplore.ieee.org/document/10861015
ディープラーニングに基づく三相電圧源インバータの動的モデリング Deep Learning-Based Dynamic Modeling of Three-Phase Voltage Source Inverters
Sunil Subedi; Liang Qiao; Yonghao Gui; Yaosuo Xue; Francis Tuffner; Wei Du
2024 IEEE Energy Conversion Congress and Exposition Date Added to IEEE Xplore: 10 February 2025
DOI:https://doi.org/10.1109/ECCE55643.2024.10861015
Abstract
Inverter-based resource (IBR) models are necessary to analyze modern power system stability and create effective control strategies. Modeling IBRs in converter-rich power systems is crucial, yet challenging due to the lack of commercial information on converter topologies and control parameters. This paper proposes novel convolutional neural network (CNN)–based data-driven techniques for modeling IBRs, addressing adaptability and proprietary concerns without requiring internal system physics knowledge. The proposed method is tested using real grid-tied commercial IBR transient data and demonstrates effectiveness and accuracy. Furthermore, the developed modeling approach is integrated and implemented in the open-source power distribution simulation and analysis tool, GridLAB-D, to illustrate the potentiality of dynamic analysis of large-scale power systems with high IBRs.
